updating

updating the ratings

packages

library(tidyverse) # CRAN v1.3.0 

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Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
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── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.3.2     ✓ purrr   0.3.4
✓ tibble  3.0.3     ✓ dplyr   1.0.0
✓ tidyr   1.1.0     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.5.0
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

======= ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ── ✓ ggplot2 3.3.1 ✓ purrr  0.3.4 ✓ tibble  3.0.1 ✓ dplyr  1.0.0 ✓ tidyr  1.1.0 ✓ stringr 1.4.0 ✓ readr  1.3.1 ✓ forcats 0.5.0 ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ── x dplyr::filter() masks stats::filter() x dplyr::lag() masks stats::lag() >>>>>>> c61506b595d43f5813e182283920fd2db29fcc57

library(progress) # CRAN v1.2.2 
library(ggrepel)
library(lubridate)

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union

library(conflicted) # CRAN v1.0.4
conflict_prefer("filter", "dplyr") 

[conflicted] Will prefer dplyr::filter over any other package

source("functions.R")
source("cleaning.R")
source("estimation.R")
source("graphs.R")
options(dplyr.summarise.inform = FALSE)

load up latest games and “pre” ratings

games <- read_rds("~/teaching/scoresway/rds/games.rds")
with_pre <- read_rds("wpre.rds")
with_pre

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updated ratings

k <- 20
options(dplyr.summarise.inform = FALSE)
pb <- progress_bar$new(format = " progress [:bar] :percent eta: :eta", total = nrow(with_pre), width = 40)
with_pre %>% 
  mutate(rat=map2(this, pre, ~this_ratings(.x, .y, k, games))) %>% 
  mutate(post=map(rat, "r")) %>% 
  mutate(updates=map(rat, "d")) -> with_post

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 progress [>-------------]   4% eta:  6s
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=======


 progress [--------------]   3% eta:  7s
 progress [>-------------]   4% eta: 10s
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 progress [>-------------]   7% eta: 11s
 progress [>-------------]   8% eta: 12s
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>>>>>>> c61506b595d43f5813e182283920fd2db29fcc57
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with_post

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save



write_rds(with_post, "wpost.rds")



read



with_post <- read_rds("wpost.rds")



look at one


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with_post %>% filter(league=="japan") %>% 
  unnest(post) %>% 
  select(team, rating, h) %>% arrange(desc(rating))

look at all

see post values sorted in tabs

since_hrs <- 24
with_post %>% 
  mutate(latest=map_dbl(updates, ~max(.$time_stamp))) %>% 
  mutate(since=(now()-as_datetime(latest))/dhours(1)) %>% 
  filter(since<=since_hrs) -> wp2
wp2

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see post values sorted in tabs

# read_csv("wanted.txt") %>% pull(want) -> wants
# wants
# with_post <- read_rds("wpost.rds")
# with_post %>% filter(league %in% wants) %>%  # edit/remove slice
wp2 %>% 
  arrange(desc(league)) %>% 
  select(post, updates, league) %>% 
  pwalk(~display(..1, ..2, ..3)) # first input to pwalk can be a df

plot

# time_graph("england 2", with_post)
map(wp2$league, ~time_graph(., with_post))

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